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Section Endocrinology, Department of Medicine, Erasmus MC, Rotterdam, The Netherlands
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, based on principal component analysis and vascular ultrasound measurements. Patients and methods Patients Patients were actively recruited as described before ( 3 ). Briefly, all long-term (5 or more years after treatment) adult survivors of
Department of Electronic Science, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen, China
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. The XCMS software was employed to convert each data file into a matrix of detected peaks. Differences in metabolic profiles on LC-MS were determined by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS
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Metabolomics was used to explore the effect of exercise intervention on type 2 diabetes. The rat model of type 2 diabetes was induced by an injection of streptozocin (30 mg/kg), after fed with 8-week high-fat diet. The rats were divided into three groups: the control group, the diabetic model group (DM) and the diabetes + exercise group (DME). After exercise for 10 weeks, blood samples were collected to test biomedical indexes, and 24-h urine samples were collected for the metabolomics experiment. In the DME group, fasting blood glucose (FBG), both total cholesterol (TC) and total plasma triglycerides (TG), were decreased significantly, compared with those in the DM group. Based on gas chromatography-mass spectrometry (GC/MS), a urinary metabolomics method was used to study the mechanism of exercise intervention on diabetes mellitus. Based on the principal component analysis (PCA), it was found that the DM group and control group were separated into two different clusters. The DME group was located between the DM group and the control group, closer to the control group. Twelve significantly changed metabolites of diabetes mellitus were detected and identified, including glycolate, 4-methyl phenol, benzoic acid, 1H-indole, arabinitol, threitol, ribonic acid, malic acid, 2,3-dihydroxy-butanoic, aminomalonic acid, l-ascorbic acid and 3-hydroxy hexanedioic acid. After exercise, seven metabolites were significantly changed, compared with the control group, the relative contents of benzoic acid, aminomalonic acid, tetrabutyl alcohol and ribonucleic acid in the diabetic exercise group decreased significantly. The relative contents of 2,3-dihydroxybutyric acid, l-ascorbic acid and 3-hydroxy adipic acid increased significantly. l-ascorbic acid and aminomalonic acid which related with the oxidative stress were significantly regulated to normal. The results showed that exercise could display anti-hyperglycemic and anti-hyperlipidemic effects. The exercise had antioxidation function in preventing the occurrence of complications with diabetes mellitus to some extent. The work illustrates that the metabolomics method is a useful tool to study the mechanism of exercise treatment.
Department of Growth and Reproduction, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
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International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
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analyses The native principal component analysis (PCA) function ‘prcomp()’ in R was applied to the SDS profiles of prepubertal controls and boys with KS in the dataset, as previously demonstrated ( 23 ). The ‘ggbiplot’ package was used to visualize the
Weihai Institute for Bionics, Jilin University, Weihai, China
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College of Biological and Agricultural Engineering, Jilin University, Changchun, China
Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun, China
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. A pattern recognition method based on principal component analysis (PCA) was used to identify gaseous VOCs in 120 healthy volunteers and 120 diabetic patients to study the ability of features to distinguish health status. PCA results showed that 60
International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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International Center for Research and Research Training in Endocrine Disruption of Male Reproduction and Child Health (EDMaRC), Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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principal component analysis on clinical and biochemical parameters exemplified in children with congenital adrenal hyperplasia . Frontiers in Endocrinology 2021 12 652888 . ( https://doi.org/10.3389/fendo.2021.652888 ) 16 van der Straaten S Springer
Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
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Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
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Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
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Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
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Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
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Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
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Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
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Department of Ophthalmology, Pingxiang People’s Hospital of Southern Medical University, Pingxiang, Jiangxi, China
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Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
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Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
Center on Clinical Research, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, China
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, we additionally performed a multiple principal component analysis (PCA) on n-6 PUFAs to total n-3 PUFAs. As we could see in Fig. 2 , the top two principle components (PC) could explain 80.8% (62.0 and 18.8% for the 1st and 2nd PC, respectively) of
Department of Endocrinology, Department of Molecular Medicine and Surgery, Metabolism and Diabetology, Karolinska University Hospital, 171 76 Stockholm, Sweden
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custom scripts. Statistical analyses The values are presented as median and range or means± s.e.m . For univariate data Student's t -test was used. All multivariate statistical analyses, i.e. principal component analysis (PCA) and orthogonal projections
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U.S. Environmental Protection Agency, Center for Public Health and Environmental Assessment, Research Triangle Park, North Carolina, USA
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index, water index, land index, built environment index, and sociodemographic index) were created by retaining the first component of a principal components analysis (PCA) that included all of the domain-specific variables. A list of variables and those
eXtraOrdinarY Kids Clinic, Children's Hospital Colorado, Aurora, Colorado, USA
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eXtraOrdinarY Kids Clinic, Children's Hospital Colorado, Aurora, Colorado, USA
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eXtraOrdinarY Kids Clinic, Children's Hospital Colorado, Aurora, Colorado, USA
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used to generate a volcano plot identifying metabolites with a fold change >1.5 and FDR <0.05. To determine if a unique metabolome profile is present in KS, partial least squares – discriminant analysis (PLS-DA), a variant of principal component